Atmospheric processes

Abstract:

This study evaluates convective cell properties and their relationships with convective and stratiform rainfall within a season‐long convection‐permitting weather research and forecasting simulation over central Argentina using radar, satellite, and radiosonde measurements from the RELAMPAGO‐CACTI field campaign. The simulation slightly underestimates radar‐estimated rainfall over the ∼3.5‐month evaluation period but underestimates stratiform rainfall by 46% and overestimates convective rainfall by 43%. As convective available potential energy (CAPE) increases, the convective rainfall overestimation decreases, but the stratiform rainfall underestimation increases such that the contribution of convective to total rainfall remains constantly high biased by ∼26%. Overestimated convective rainfall arises from the simulation generating 2.6 times more precipitating convective cells (14,299) than observed by radar (5,662) despite similar observed and simulated cell growth processes, with relatively wide cells contributing mostly to excessive convective rainfall. Relatively shallow cells, typically reaching heights of 4–7 km, contribute most to the cell number bias. This cell number bias increases as CAPE decreases, potentially because cells and their updrafts become narrower and more under‐resolved as CAPE decreases. The gross overproduction of precipitating shallow cells leads to overly efficient precipitation and inadequate detrainment of ice aloft, thereby diminishing the formation of robust stratiform rainfall regions. Decreasing model horizontal grid spacing from 3 to 1 or 0.333 km for low (<300 J kg−1) and high CAPE (>1,000 J kg−1) cases results in minimal change to cell number, depth, and convective‐to‐stratiform partitioning biases. This suggests that improving prediction of these convective properties depends on factors beyond solely increasing model resolution.

Abstract:

The mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two-dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse-graining methodology, we are able to present a spatio-temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using “conditioned spectra.” These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation.

Abstract:

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included.

Abstract:

Abstract:

Clouds are one of the largest sources of uncertainty in climate predictions. Global km‐scale models need to simulate clouds and precipitation accurately to predict future climates. To isolate issues in their representation of clouds, models need to be thoroughly evaluated with observations. Here, we introduce multifractal analysis as a method for evaluating km‐scale simulations. We apply it to outgoing longwave radiation fields to investigate structural differences between observed and simulated anvil clouds. We compute fractal parameters which compactly characterize the scaling behavior of clouds and can be compared across simulations and observations. We use this method to evaluate the nextGEMS ICON simulations via comparison with observations from the geostationary satellite GOES‐16. We find that multifractal scaling exponents in the ICON model are significantly lower than in observations. We conclude that too much variability is contained in the small scales ( < 100 k m ) $(< 100\ \mathrm{k}\mathrm{m})$ leading to less organized convection and smaller, isolated anvils.